Detection of incidents and events in urban networks

Events and incidents are relatively rare, but they often have a negative impact on traffic. Reliable travel demand predictions during events and incident detection algorithms are thus essential. The authors study link flows that were collected throughout the Dutch city of Almelo. We show that reliable, event-related demand forecasting is possible, but predictions can be improved if exact start and end times of events are known, and demand variations are monitored conscientiously. For incident detection, we adopt a method that is based on the detection of outliers. Our algorithm detects most outliers, while the fraction of detections due to noisy data is only a few percent. Although our method is less suitable for automatic incident detection, it can be used in an urban warning system that alerts managers in case of a possible incident. It also enables us to study incidents off-line. In doing so, we find that a significant fraction of traffic changes route during an incident.

[1]  Yong Shi,et al.  ON-LINE TESTING OF THE MCMASTER INCIDENT DETECTION ALGORITHM UNDER RECURRENT CONGESTION , 1993 .

[2]  Panos G Michalopoulos,et al.  COMPARATIVE PERFORMANCE EVALUATION OF INCIDENT DETECTION ALGORITHMS , 1992 .

[3]  A. Oostveen,et al.  A new incident detection scheme developed in the Netherlands , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[4]  Samuel C Tignor,et al.  FREEWAY INCIDENT-DETECTION ALGORITHMS BASED ON DECISION TREES WITH STATES , 1978 .

[5]  A. Ash.  Incident detection in urban areas controlled by SCOOT , 1997 .

[6]  Carroll J Messer,et al.  Incident detection on urban freeways , 1974 .

[7]  Dieter Wild,et al.  SHORT-TERM FORECASTING BASED ON A TRANSFORMATION AND CLASSIFICATION OF TRAFFIC VOLUME TIME SERIES , 1997 .

[8]  Padhraic Smyth,et al.  Adaptive event detection with time-varying poisson processes , 2006, KDD '06.

[9]  Baher Abdulhai,et al.  Comparison and Analysis Tool for Automatic Incident Detection , 2005 .

[10]  Tom Thomas,et al.  Periodic variations in urban traffic flows , 2006 .

[11]  Ramkumar Venkatanarayana,et al.  CHARACTERIZATION OF FREEWAY CAPACITY REDUCTION RESULTING FROM TRAFFIC ACCIDENTS , 2003 .

[12]  D. Bowers,et al.  TRAFFIC CONGESTION INCIDENT DETECTION , 1996 .

[13]  Weiming Liu,et al.  Improved freeway incident detection using neural network based on pulse data of the loop detector , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[14]  W. A. M. Weijermars.  Analysis of Urban Traffic Patterns Using Clustering , 2007 .

[15]  Stephen G. Ritchie,et al.  Simulation Of Freeway Incident Detection Using Artificial Neural Networks , 1993 .